Noise reduction is an essential part of video pre-processing prior to encoding. It is based on recursive time filtering.
The noise reduction techniques are generally carried out on digital video images in the form of a matrix of samples; each sample is composed of a luminance signal and, in the case of a colour signal, a chrominance signal.
Even today, the acquisition of video image sequences is still broadly carried out in an analogue form so that, once they have been acquired and optionally transmitted and stored in analogue formats, the images contain a substantial noise component. Once they have been digitized, these images are often also subjected to storage/editing operations which in turn introduce noise, this time of a digital nature. Lastly, a sequence of images undergoes a succession of transformations which lead to spatiotemporal noise of a highly random nature.
In order to obtain good results, the noise reduction methods which use recursive filtering address the very high temporal correlation of the images in a video sequence. The ideas of motion and displacement are therefore important with a view to developing effective noise reduction.
“Displacement” is intended to mean an object's change of position in a scene, this change of position being localized and specific to this object. “Motion” is intended to mean all the displacements of objects in a video sequence, taken together.
The motion is conventionally detected either by simple image-to-image differencing, or by using a motion estimator.
When a motion estimator is used, the displacements are accounted for by taking image differences at separate times, as well as by moving spatially through the frames. These displacements are represented by motion vector fields relating to pixels (motion estimation per pixel) or to blocks (motion estimation in blocs). This provides motion-compensated image differences, referred to as DFDs (Displacement Frame Differences), for pixels or for blocks.
A motion estimator has imperfections, however, which may lead to output defects of the recursive filter, and which are propagated through time by the principle of recursion. Examples of these imperfections are the problems of object tracking, one object being masked by another, and the appearance of a new object.
One solution envisaged in order to overcome these drawbacks is to apply the recursive filter only to fixed regions or regions with little motion. The noise reducer is then a motion-adapted noise reducer, instead of a motion-compensated noise reducer. A major drawback of such a method is that the noise is only removed from sequences without motion or with little motion, or from regions of images, but, if there is noise in a sequence, then the noise will be present throughout the sequence. Such a noise reducer would not therefore be very effective.